BigWig Tracks
BigWig is an indexed binary format for continuous genomic data. Efficient for genome browsers and programmatic access.
Why BigWig?
Format Size Random Access Browser Support
bedGraph Large No Limited
bigWig ~10x smaller Yes (indexed) Excellent
Convert bedGraph to bigWig (CLI)
Installation
UCSC tools
conda install -c bioconda ucsc-bedgraphtobigwig ucsc-bigwigtobedgraph
Or download directly
wget http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig chmod +x bedGraphToBigWig
Basic Conversion
Sort bedGraph first (required)
sort -k1,1 -k2,2n coverage.bedGraph > coverage.sorted.bedGraph
Convert to bigWig
bedGraphToBigWig coverage.sorted.bedGraph chrom.sizes output.bw
chrom.sizes format: chr<TAB>size
chr1 248956422
chr2 242193529
Get Chromosome Sizes
From FASTA index
cut -f1,2 reference.fa.fai > chrom.sizes
Download from UCSC
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
From BAM header
samtools view -H alignments.bam | grep @SQ | sed 's/@SQ\tSN:|LN://g' > chrom.sizes
Full Workflow
Generate bedGraph from BAM
bedtools genomecov -ibam alignments.bam -bg > coverage.bedGraph
Sort bedGraph
sort -k1,1 -k2,2n coverage.bedGraph > coverage.sorted.bedGraph
Convert to bigWig
bedGraphToBigWig coverage.sorted.bedGraph hg38.chrom.sizes coverage.bw
Clean up intermediate files
rm coverage.bedGraph coverage.sorted.bedGraph
Read BigWig with pyBigWig (Python)
Installation
pip install pyBigWig
Open and Inspect
import pyBigWig
Open file
bw = pyBigWig.open('coverage.bw')
File info
print(f'Chromosomes: {bw.chroms()}') print(f'Header: {bw.header()}')
Check if file is bigWig (not bigBed)
print(f'Is bigWig: {bw.isBigWig()}')
Close when done
bw.close()
Extract Values
import pyBigWig
bw = pyBigWig.open('coverage.bw')
Get values for a region (returns numpy array)
values = bw.values('chr1', 1000000, 1001000) print(f'Mean: {values.mean():.2f}') print(f'Max: {values.max():.2f}')
Get specific intervals with values
intervals = bw.intervals('chr1', 1000000, 1001000)
Returns: [(start, end, value), ...]
for start, end, val in intervals: print(f'{start}-{end}: {val}')
Statistics for region
stats = bw.stats('chr1', 1000000, 1001000, type='mean') print(f'Mean coverage: {stats[0]:.2f}')
Available stat types: mean, min, max, coverage, std, sum
max_val = bw.stats('chr1', 1000000, 1001000, type='max') coverage = bw.stats('chr1', 1000000, 1001000, type='coverage')
bw.close()
Binned Statistics
import pyBigWig
bw = pyBigWig.open('coverage.bw')
Get mean values in 100bp bins across region
region_start, region_end = 1000000, 2000000 n_bins = 1000 # 100bp bins
binned = bw.stats('chr1', region_start, region_end, type='mean', nBins=n_bins)
Returns list of n_bins values
bw.close()
Extract for BED Regions
import pyBigWig import pybedtools
bw = pyBigWig.open('coverage.bw') bed = pybedtools.BedTool('regions.bed')
Get mean signal per region
results = [] for interval in bed: chrom, start, end = interval.chrom, interval.start, interval.end mean_signal = bw.stats(chrom, start, end, type='mean')[0] results.append({ 'chrom': chrom, 'start': start, 'end': end, 'name': interval.name, 'signal': mean_signal if mean_signal else 0 })
bw.close()
Convert to DataFrame
import pandas as pd df = pd.DataFrame(results) print(df)
Create BigWig with pyBigWig
import pyBigWig
Create new bigWig
bw = pyBigWig.open('output.bw', 'w')
Add header (chromosome sizes)
bw.addHeader([('chr1', 248956422), ('chr2', 242193529)])
Add entries (must be sorted by position)
Method 1: Individual entries
bw.addEntries(['chr1', 'chr1'], [0, 100], ends=[100, 200], values=[1.5, 2.3])
Method 2: Chromosome at a time (more efficient)
bw.addEntries('chr1', [0, 100, 200], ends=[100, 200, 300], values=[1.5, 2.3, 3.1])
Method 3: Fixed-width spans (most efficient for dense data)
bw.addEntries('chr2', 0, values=[1.0, 2.0, 3.0, 4.0], span=100, step=100)
Creates: chr2:0-100=1.0, chr2:100-200=2.0, chr2:200-300=3.0, chr2:300-400=4.0
bw.close()
deepTools for BigWig Operations
Installation
conda install -c bioconda deeptools
Generate Normalized BigWig from BAM
RPKM normalization
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing RPKM
CPM normalization
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing CPM
BPM (bins per million) - like TPM for ChIP-seq
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing BPM
With bin size and smoothing
bamCoverage -b alignments.bam -o coverage.bw
--binSize 10
--normalizeUsing CPM
--smoothLength 30
Extend reads to fragment length
bamCoverage -b alignments.bam -o coverage.bw
--extendReads 200
--normalizeUsing CPM
Compare BigWig Files
Log2 ratio of two bigWig files
bigwigCompare -b1 treatment.bw -b2 control.bw -o log2ratio.bw --ratio log2
Subtract
bigwigCompare -b1 treatment.bw -b2 control.bw -o diff.bw --ratio subtract
Mean of multiple files
bigwigAverage -b file1.bw file2.bw file3.bw -o average.bw
Summarize BigWig Over Regions
Matrix for heatmap (signal around regions)
computeMatrix reference-point -S signal.bw -R regions.bed
-b 2000 -a 2000 -o matrix.gz
Plot heatmap
plotHeatmap -m matrix.gz -o heatmap.png
Summary statistics per region
multiBigwigSummary BED-file -b sample1.bw sample2.bw -o results.npz --BED regions.bed
Convert BigWig to bedGraph
Using UCSC tool
bigWigToBedGraph input.bw output.bedGraph
Extract specific region
bigWigToBedGraph input.bw output.bedGraph -chrom=chr1 -start=1000000 -end=2000000
Common Patterns
ChIP-seq Signal Track
Generate normalized track
bamCoverage -b chip.bam -o chip.bw
--normalizeUsing CPM
--extendReads 200
--binSize 10
Generate input-subtracted track
bigwigCompare -b1 chip.bw -b2 input.bw -o chip_minus_input.bw --ratio subtract
RNA-seq Coverage
Strand-specific coverage
bamCoverage -b rnaseq.bam -o forward.bw --filterRNAstrand forward bamCoverage -b rnaseq.bam -o reverse.bw --filterRNAstrand reverse
Extract Signal for Analysis
import pyBigWig import pandas as pd
def extract_signal(bw_path, bed_path, stat='mean'): '''Extract bigWig signal for BED regions.''' import pybedtools bw = pyBigWig.open(bw_path) bed = pybedtools.BedTool(bed_path)
results = []
for interval in bed:
val = bw.stats(interval.chrom, interval.start, interval.end, type=stat)[0]
results.append({
'chrom': interval.chrom,
'start': interval.start,
'end': interval.end,
'name': interval.name if interval.name else '.',
'signal': val if val is not None else 0
})
bw.close()
return pd.DataFrame(results)
Usage
df = extract_signal('coverage.bw', 'peaks.bed', stat='mean') print(df)
Related Skills
-
coverage-analysis - Generate bedGraph input
-
chip-seq/chipseq-visualization - ChIP-seq signal tracks
-
alignment-files/bam-statistics - BAM to coverage
-
interval-arithmetic - Region operations